DP-300 Administering Relational Databases on Microsoft Azure Questions and Answers

The lifecycle management policy lets you:

Delete blobs, blob versions, and blob snapshots at the end of their lifecycles

There are CPU and Data I/O spikes for the PaaS prototype. Business Critical 4-vCore is needed.

Scenario: Contoso requirements for the sales transaction dataset include:

Implement a surrogate key to account for changes to the retail store addresses.

A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the

table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.

Box 1: Yes

DBAGroup1 is member of the Contributor role.

The Contributor role grants full access to manage all resources, but does not allow you to assign roles in Azure RBAC, manage assignments in Azure Blueprints, or share image galleries.

Box 2: No

Box 3: Yes

DBAGroup2 is member of the SQL DB Contributor role.

The SQL DB Contributor role lets you manage SQL databases, but not access to them. Also, you can't manage their security-related policies or their parent SQL servers. As a member of this role you can create and manage SQL databases.

Scenario: Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.

To monitor the performance of a database in Azure SQL Database and Azure SQL Managed Instance, start by monitoring the CPU and IO resources used by your workload relative to the level of database performance you chose in selecting a particular service tier and performance level.

Scenario: Use a single dashboard to review security and audit data for all the PaaS databases.

With dashboards can bring together operational data that is most important to IT across all your Azure resources, including telemetry from Azure Log Analytics.

Note: Auditing for Azure SQL Database and Azure Synapse Analytics tracks database events and writes them to an audit log in your Azure storage account, Log Analytics workspace, or Event Hubs.

Azure SQL Database single database supports Data Sync.

The exhibits displays Database [State] properties.

To query a dynamic management view or function requires SELECT permission on object and VIEW SERVER STATE or VIEW DATABASE STATE permission.

Scenario: The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.

Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Box 1: HASH

Box 2: OrderDateKey

In most cases, table partitions are created on a date column.

A way to eliminate rollbacks is to use Metadata Only operations like partition switching for data management. For example, rather than execute a DELETE statement to delete all rows in a table where the order_date was in October of 2001, you could partition your data early. Then you can switch out the partition with data for an empty partition from another table.

https://learn.microsoft.com/en-us/azure/site-recovery/site-recovery-sql#combining-bcdr-technologies-with-site-recovery

Send application metrics using Dropwizard.

Spark uses a configurable metrics system based on the Dropwizard Metrics Library.

To send application metrics from Azure Databricks application code to Azure Monitor, follow these steps:

Step 1: Configure your Azure Databricks cluster to use the Databricksmonitoring library.

Prerequisite: Configure your Azure Databricks cluster to use the monitoring library.

Step 2: Build the spark-listeners-loganalytics-1.0-SNAPSHOT.jar JAR file

Step 3: Create Dropwizard counters in your application code

Create Dropwizard gauges or counters in your application code

A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:

What resources the client may access.

What permissions they have to those resources.

How long the SAS is valid.

Note: Data Lake Storage Gen2 supports the following authorization mechanisms:

Shared Key authorization

Shared access signature (SAS) authorization

Role-based access control (Azure RBAC)

Access control lists (ACL) Data Lake Storage Gen2 supports the following authorization mechanisms:

Shared Key authorization

Shared access signature (SAS) authorization

Role-based access control (Azure RBAC)

Access control lists (ACL)

Transparent data encryption (TDE) helps protect Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics against the threat of malicious offline activity by encrypting data at rest.

Yes

No

No

To generate an email alert to admin@contoso.com when CPU percentage utilization for db1 is higher than average, you can use the Azure portal to create an alert rule based on the CPU percentage metric. Here are the steps to do that:

Go to the Azure portal and select your Azure SQL Database server that hosts db1.

Select Alerts in the Monitoring section and click on New alert rule.

In the Condition section, click Add and select the CPU percentage metric.

In the Configure signal logic page, set the threshold type to Dynamic. This will compare the current metric value to the historical average and trigger the alert when it deviates significantly1.

Set the operator to Greater than, the aggregation type to Average, the aggregation granularity to 1 minute, and the frequency of evaluation to 5 minutes.

Click Done to save the condition.

In the Action group section, click Create and enter a name and a short name for the action group.

In the Notifications section, click Add and select Email/SMS message/Push/Voice.

Enter admin@contoso.com in the Email field and click OK.

Click OK to save the action group.

In the Alert rule details section, enter a name and a description for the alert rule, choose a severity level, and make sure the rule is enabled.

Click Create alert rule to create the alert rule.

This alert rule will send an email to admin@contoso.com when the CPU percentage utilization for db1 is higher than average. You can also add other actions to the alert rule, such as calling a webhook or running an automation script

To prevent db1 from reverting to the last known good query plan, you need to disable the automatic plan correction feature for the database. This feature is enabled by default and allows the Query Store to detect and fix plan performance regressions by forcing the last good plan1. However, if you want to test the performance of different plans without interference from the Query Store, you can turn off this feature by using the ALTER DATABASE SCOPED CONFIGURATION statement2.

Here are the steps to disable the automatic plan correction feature for db1:

Connect to db1 using SQL Server Management Studio, Azure Data Studio, or any other tool that supports Transact-SQL statements.

Open a new query window and run the following command: ALTER DATABASE SCOPED CONFIGURATION SET AUTOMATIC_TUNING (FORCE_LAST_GOOD_PLAN = OFF); GO

This command will disable the automatic plan correction feature for db1 and allow the Query Optimizer to choose the best plan based on the current statistics and parameters3.

To verify that the automatic plan correction feature is disabled for db1, you can query the sys.database_scoped_configurations catalog view. The value of the force_last_good_plan column should be 0 for db1.

These are the steps to disable the automatic plan correction feature for db1.

To ensure that all queries executed against dbl are captured in the Query Store, you need to enable the Query Store feature for the database and set the query capture mode to ALL. The Query Store feature provides you with insight on query plan choice and performance for Azure SQL Database1. The query capture mode controls whether all queries or only a subset of queries are tracked2.

Here are the steps to enable the Query Store and set the query capture mode to ALL for the database dbl:

Using the Azure portal:

Go to the Azure portal and select your Azure SQL Database server.

Select the database dbl and click on Query Performance Insight in the left menu.

Click on Configure Query Store and turn on the Query Store switch.

In the Query Capture Mode dropdown, select All and click on Save.

Using Transact-SQL statements:

Connect to the Azure SQL Database server and the database dbl using SQL Server Management Studio or Azure Data Studio.

Run the following command to enable the Query Store for the database: ALTER DATABASE dbl SET QUERY_STORE = ON;

Run the following command to set the query capture mode to ALL for the database: ALTER DATABASE dbl SET QUERY_STORE (QUERY_CAPTURE_MODE = ALL);

These are the steps to ensure that all queries executed against dbl are captured in the Query Store.

SalesSQLDb1 experiences performance issues that are likely due to out-of-date statistics and frequent blocking queries.

A: Use sys.dm_pdw_nodes_tran_locks instead of sys.dm_tran_locks from Azure Synapse Analytics (SQL Data Warehouse) or Parallel Data Warehouse.

E: Example:

The following query will show blocking information.

SELECT

t1.resource_type,

t1.resource_database_id,

t1.resource_associated_entity_id,

t1.request_mode,

t1.request_session_id,

t2.blocking_session_id

FROM sys.dm_tran_locks as t1

INNER JOIN sys.dm_os_waiting_tasks as t2

ON t1.lock_owner_address = t2.resource_address;

Note: Depending on the system you’re working with you can access these wait statistics from one of three locations:

sys.dm_os_wait_stats: for SQL Server

sys.dm_db_wait_stats: for Azure SQL Database

sys.dm_pdw_nodes_os_wait_stats: for Azure SQL Data Warehouse

Scenario: SalesSQLDb1 uses database firewall rules and contained database users.

Box 1: DTU

Scenario:

The 30 new databases must scale automatically.

Once all requirements are met, minimize costs whenever possible.

You can configure resources for the pool based either on the DTU-based purchasing model or the vCore-based purchasing model.

In short, for simplicity, the DTU model has an advantage. Plus, if you’re just getting started with Azure SQL Database, the DTU model offers more options at the lower end of performance, so you can get started at a lower price point than with vCore.

Box 2: An Azure SQL database elastic pool

Azure SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single server and share a set number of resources at a set price. Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database.

Automating Azure SQL DB index and statistics maintenance using Azure Automation:

1. Create Azure automation account (Step 1)

2. Import SQLServer module (Step 2)

3. Add Credentials to access SQL DB

This will use secure way to hold login name and password that will be used to access Azure SQL DB

4. Add a runbook to run the maintenance (Step 3)

Steps: 1. Click on "runbooks" at the left panel and then click "add a runbook"

2. Choose "create a new runbook" and then give it a name and choose "Powershell" as the type of the runbook and then click on "create"

5. Schedule task (Step 4)

Steps:1. Click on Schedules2. Click on "Add a schedule" and follow the instructions to choose existing schedule or create a new schedule.

Scenario: Business Requirements

Litware identifies business requirements include: meet an SLA of 99.99% availability for all Azure deployments.

Box 1: Cloud witness

If you have a Failover Cluster deployment, where all nodes can reach the internet (by extension of Azure), it is recommended that you configure a Cloud Witness as your quorum witness resource.

Box 2: Azure Basic Load Balancer

Microsoft guarantees that a Load Balanced Endpoint using Azure Standard Load Balancer, serving two or more Healthy Virtual Machine Instances, will be available 99.99% of the time.

Note: There are two main options for setting up your listener: external (public) or internal. The external (public) listener uses an internet facing load balancer and is associated with a public Virtual IP (VIP) that is accessible over the internet. An internal listener uses an internal load balancer and only supports clients within the same Virtual Network.

Azure Database Migration service

Box 1: Premium 4-VCore

Scenario: Migrate the SERVER1 databases to the Azure SQL Database platform.

Minimize downtime during the migration of the SERVER1 databases.

Premimum 4-vCore is for large or business critical workloads. It supports online migrations, offline migrations, and faster migration speeds.

Scenario: Authenticate database users by using Active Directory credentials.

The configuration steps include the following procedures to configure and use Azure Active Directory authentication.

Create and populate Azure AD.

Optional: Associate or change the active directory that is currently associated with your Azure Subscription.

Create an Azure Active Directory administrator. (Step 1)

Configure your client computers.

Create contained database users in your database mapped to Azure AD identities. (Step 2)

Connect to your database by using Azure AD identities. (Step 3)

Box 1: The server, the elastic pool, and the database

Senario:

SalesSQLDb1 is in an elastic pool named SalesSQLDb1Pool.

Litware technical requirements include: all SQL Server and Azure SQL Database metrics related to CPU and storage usage and limits must be analyzed by using Azure built-in functionality.

Box 2: Azure Event hubs

Scenario: Migrate ManufacturingSQLDb1 to the Azure virtual machine platform.

Event hubs are able to handle custom metrics.

Scenario: Authenticate database users by using Active Directory credentials.

(Create a new Azure SQL database named ResearchDB1 on a logical server named ResearchSrv01.)

Authenticate the user in SQL Database or SQL Data Warehouse based on an Azure Active Directory user:

CREATE USER [Fritz@contoso.com] FROM EXTERNAL PROVIDER;